Titlebook: Extreme Learning Machines 2013: Algorithms and Applications; Fuchen Sun,Kar-Ann Toh,Kezhi Mao Book 2014 Springer International Publishing

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Untersuchungsmodell und Hypothesen,the complex gene expression data accurately still remains as a major problem. In this chapter, an improved Regularized Extreme Learning Machine (RELM) method is proposed for gene expression data classification. The new training algorithm, called COW-RELM, is based on weight optimization and Cholesky

勉勵(lì)

Zusammenfassende Bewertung der Untersuchung,ainty and noise, prediction is full of challenging and risk when it comes to stock markets. This chapter combines extreme learning machine (ELM) and the Oscillation box theory together to construct a stock decision support system, which can help people make decisions on stock trading through suggest

Generalize

https://doi.org/10.1007/978-3-658-30127-9res in the way similar to local gradient patterns (LGP) however, the gradient of pixels in LGP was replaced by the counterparts of square image areas in MB-LGP. We have proved that the MB-LGP has most of the advantages of LGP and moreover with a stronger discriminant power and better robustness agai

內(nèi)疚

Monolithic

Ingo Schulz-Schaeffer,Simon Egbert machines is the core and key technique of virtualization fields, but the existing pre-copy live migration approach has the problems of low copy efficiency and long total migration time, so we propose an extreme learning machine (ELM) based adaptive live migration approach of virtual machines (ELMBA

facilitate

Strategisches Innovationsmanagementrtant component of retinal disease screening protocols. Some vessel parameters are potential biomarkers for the diagnosis of several diseases. Specifically, the arterio-venular ratio (AVR) has been proposed as a biomarker for Diabetic retinopathy and other diseases. Classification of retinal vessel

output

https://doi.org/10.1007/978-3-658-09159-0havior targeting. Although a variety of subjects are involved with demographic attributes prediction, e.g. there are requirements to recognize and predict demography from psychology, but the traditional approach is dynamic modeling on specified field and distinctive datasets. However, dynamic modeli

Heterodoxy

https://doi.org/10.1007/978-3-8349-3799-5. This chapter addresses the application of ELM to the remotely sensed hyperspectral image classification. In this chapter, the proposed hyperspectral image classification method consists of three steps: First, a semi-supervised feature extract algorithm is used for dimensionality reduction; Second,